In this section, the performances of the proposed algorithms (GGM/GGFM and EGM/EGFM) are evaluated on three real-world data sets consisting of \((i)\)  animal species, \((ii)\)  GNSS time series and \((iii)\)  concepts.
The purpose of the following experiments is to verify whether the factor model, which accounts for potential structures in the covariance matrix, provides an improved version of the learned graph topology. The latter is evaluated using both graph modularity \(m\) , for which high values measure high separability of a graph into sub-components [1]}, and visual inspectionNote that there is no available ground truth for these data sets. Hence, to facilitate visualization, each graph node is then clustered into colors using a community detection algorithm based on label propagation [2]}..
